共同信息（MI）已被广泛用作训练神经网络的损失正规化程序。当学习高维数据的分解或压缩表示时，这特别有效。但是，差异熵（DE）是信息的另一种基本衡量标准，在神经网络培训中尚未发现广泛使用。尽管DE提供了比MI的可能更广泛的应用程序，但现成的DE估计器要么是非可区分的，在计算上是棘手的，要么无法适应基础分布的变化。这些缺点使它们无法在神经网络培训中用作正规化器。为了解决DE先前提出的估计器中的缺点，我们在这里介绍了刀具，这是一个完全参数化的，基于DE的基于核的估计器。我们方法的灵活性还使我们能够为条件（离散变量或连续变量）以及MI构建基于刀的估计器。我们从经验上验证了高维合成数据的方法，并进一步应用它来指导神经网络的现实任务培训。我们对各种任务的实验，包括视觉域的适应性，文本公平分类和文本微调，证明了基于刀的估计的有效性。代码可以在https://github.com/g-pichler/knife上找到。

功耗数据非常有用，因为它允许优化电网，检测异常并防止故障，最重要的是，对不同的研究目的是有用的。然而，使用功耗数据提高了重大隐私问题，因为此数据通常属于电力公司的客户。作为解决方案，我们提出了一种方法来生成忠实地模仿原件的合成功耗样本，而是从客户端及其身份中脱离。我们的方法基于生成的对抗性网络（GANS）。我们的贡献是双重的。首先，我们专注于所生成数据的质量，这不是一个简单的任务，因为没有可用的标准评估方法。然后，我们研究了向我们神经网络培训集成员提供的隐私保障。作为隐私的最低要求，我们要求我们的神经网络对成员推论攻击强大，因为除了呈现自己的隐私威胁之外，对于进一步的攻击，提供了一个网关。我们发现，在算法提供的隐私和绩效之间存在妥协。

基于样本的连续分布信息衡量估算是统计和机器学习中的一个基本问题。在本文中，当概率密度函数属于预定的凸面族{P} $时，我们分析了从有限数量的样本计算的差分熵的估计。首先，如果$ \ mathcal {p} $的密度差异熵是无限的，显然表达出额外假设的必要性，则估计差动熵将是不可行的。随后，我们调查了足够的条件，使差动熵估计能够置信界限。特别地，假设概率密度函数是LipsChitz恒定和已知的界限支持的概率密度函数是LipsChitz的差分熵的简单直方图估计的基于差分熵的差分估计。我们的重点是在差分熵上，但我们提供了示例，表明相似的结果适用于相互信息和相对熵。

最小化分布匹配损失是在图像分类的背景下的域适应的原则方法。但是，在适应分割网络中，它基本上被忽略，目前由对抗模型主导。我们提出了一系列损失函数，鼓励在网络输出空间中直接核心密度匹配，直至从未标记的输入计算的一些几何变换。我们的直接方法而不是使用中间域鉴别器，而不是使用单一损失统一分发匹配和分段。因此，它通过避免额外的对抗步骤来简化分段适应，同时提高培训的质量，稳定性和效率。我们通过网络输出空间的对抗培训使我们对最先进的分段适应的方法并置。在对不同磁共振图像（MRI）方式相互调整脑细分的具有挑战性的任务中，我们的方法在准确性和稳定性方面取得了明显的结果。

我们研究了由Biclesting问题激励的新型多终端源编码设置。两个单独的编码器观察两个i.i.d.分别序列$ x ^ n $和$ y ^ n $。目标是找到速率有限的编码$ f（x ^ n）$和$ g（z ^ n）$，最大化相互信息$ i（f（x ^ n）; g（y ^ n））/ n$。我们讨论了对独立性，模式识别和信息瓶颈方法的假设检验的这个问题的联系。改善内部和外界的先前基数界限使我们能够彻底地研究二进制对称源的特殊情况，并在这个特殊情况下量化内部和外部边界之间的间隙。此外，我们调查了互信息约束的首席运营官（CEO）问题的多个描述（MD）延伸。令人惊讶的是，这个MD-CEO问题允许了可实现的区域的紧密单信表征。

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.

Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).

We show how the inherent, but often neglected, properties of large-scale LiDAR point clouds can be exploited for effective self-supervised representation learning. To this end, we design a highly data-efficient feature pre-training backbone that significantly reduces the amount of tedious 3D annotations to train state-of-the-art object detectors. In particular, we propose a Masked AutoEncoder (MAELi) that intuitively utilizes the sparsity of the LiDAR point clouds in both, the encoder and the decoder, during reconstruction. This results in more expressive and useful features, directly applicable to downstream perception tasks, such as 3D object detection for autonomous driving. In a novel reconstruction scheme, MAELi distinguishes between free and occluded space and leverages a new masking strategy which targets the LiDAR's inherent spherical projection. To demonstrate the potential of MAELi, we pre-train one of the most widespread 3D backbones, in an end-to-end fashion and show the merit of our fully unsupervised pre-trained features on several 3D object detection architectures. Given only a tiny fraction of labeled frames to fine-tune such detectors, we achieve significant performance improvements. For example, with only $\sim800$ labeled frames, MAELi features improve a SECOND model by +10.09APH/LEVEL 2 on Waymo Vehicles.

Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other visual modalities, such as lidar data, has received less attention, prohibited by the lack of text-lidar datasets. In this work, we propose LidarCLIP, a mapping from automotive point clouds to a pre-existing CLIP embedding space. Using image-lidar pairs, we supervise a point cloud encoder with the image CLIP embeddings, effectively relating text and lidar data with the image domain as an intermediary. We show the effectiveness of LidarCLIP by demonstrating that lidar-based retrieval is generally on par with image-based retrieval, but with complementary strengths and weaknesses. By combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios under adverse sensor conditions. We also use LidarCLIP as a tool to investigate fundamental lidar capabilities through natural language. Finally, we leverage our compatibility with CLIP to explore a range of applications, such as point cloud captioning and lidar-to-image generation, without any additional training. We hope LidarCLIP can inspire future work to dive deeper into connections between text and point cloud understanding. Code and trained models available at https://github.com/atonderski/lidarclip.

The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how -- by exploiting the (potentially) partial cardinal and partial probabilistic information -- more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.